Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Partnerships and Cooperations
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Section: New Results

Exploiting Content from the Web

One of our main domain of application is that of Web content. We investigate methods to acquire and exploit content from the Web.

In [30], we analyze form-based websites to discover sequences of actions and values that result in a valid form submission. Rather than looking at the text or DOM structure of the form, our method is driven by solving constraints involving the underlying client-side JavaScript code. In order to deal with the complexity of client-side code, we adapt a method from program analysis and testing, concolic testing, which mixes concrete code execution, symbolic code tracing, and constraint solving to find values that lead to new code paths. While concolic testing is commonly used for detecting bugs in stand-alone code with developer support, we show how it can be applied to the very different problem of filling Web forms. We evaluate our system on a benchmark of both real and synthetic Web forms.

In [21], we investigate focused crawling: collecting as many Web pages relevant to a target topic as possible while avoiding irrelevant pages, reflecting limited resources available to a Web crawler. We improve on the efficiency of focused crawling by proposing an approach based on reinforcement learning. Our algorithm evaluates hyperlinks most profitable to follow over the long run, and selects the most promising link based on this estimation. To properly model the crawling environment as a Markov decision process, we propose new representations of states and actions considering both content information and the link structure. The size of the state-action space is reduced by a generalization process. Based on this generalization, we use a linear-function approximation to update value functions. We investigate the trade-off between synchronous and asynchronous methods. In experiments, we compare the performance of a crawling task with and without learning; crawlers based on reinforcement learning show better performance for various target topics.

Finally, in [23], [24] we propose a framework to follow the dynamics of vanished Web communities, based on the exploration of corpora of Web archives. To achieve this goal, we define a new unit of analysis called Web fragment: a semantic and syntactic subset of a given Web page, designed to increase historical accuracy. This contribution has practical value for those who conduct large-scale archive exploration (in terms of time range and volume) or are interested in computational approaches to Web history and social science.